1. Technical Field
The present invention relates to neural networks and more particularly to systems and method for predicting semantic labels, without the use of a parser, by employing neural networks.
2. Description of the Related Art
Semantic understanding plays an important role in many end-user applications involving text: for information extraction, web-crawling systems, question and answer based systems, as well as machine translation, summarization and search. Such applications typically have to be computationally inexpensive to deal with an enormous quantity of data, e.g., web-based systems process large numbers of documents, while interactive human-machine applications require almost instant responses. Another issue is the cost of producing labeled training data needed for statistical models, which is exacerbated when those models also depend on syntactic features which must themselves be learned.
This is an illustration of semantic role labeling for the sentence “The company bought sugar on the world market to meet export commitments.” It may be labeled as follows: [The company]ARG0 [bought]REL [sugar]ARG1 [on the world market]ARGM-LOC [to meet export commitments]ARGM-PNC. ARG0 is typically an actor, REL an action, ARG1 an object, and ARGM describes various modifiers such as location (LOC) and purpose (PNC).
To achieve the goal of semantic understanding, the current consensus is to divide and conquer the problem. Researchers tackle several layers of processing tasks ranging from the syntactic, such as part-of-speech labeling and parsing, to the semantic: word-sense disambiguation, semantic role-labeling, named entity extraction, co-reference resolution and entailment. None of these tasks are end goals in themselves but can be seen as layers of feature extraction that can help in a language-based end application, such as those described above.
Unfortunately, the state-of-the-art solutions of many of these tasks are simply too slow to be used in many applications (e.g., as previously described). For example, state-of-the-art syntactic parsers theoretically have cubic complexity in the sentence length and several semantic extraction algorithms use the parse tree as an initial feature.